The proposed VC network is trained using the ADAM optimizer [1]} (learning rate is e-4, \(\beta _1 = 0.9\) , \(\beta _2 = 0.98\) ) with a batch size of 16 for 800k steps.
We set \(\alpha =0.1, \beta =0.1, \gamma =0.01\)  for equation (REF ) and use a pretrained
WaveNet [2]} vocoder to convert the output mel-spectrogram back to the waveform.
Due to the lack of explicit labels of the speech components, the degree of SRD are hard to evaluate [3]}.
Therefore, we focus on the timbre conversion by comparing with other one-shot VC models (Please note that we are able to transfer four representations of the one-shot VC as the demo shows, not just the timbre of target speaker).
We compare our proposed method
with AutoVC [4]}, ClsVC[5]}, AdaIN-VC [6]}, VQVC+ [7]} and VQMIVC [8]}, which are among the
state-of-the-art one-shot VC methods.
